\author{Daniel L Elliott}
\email{dane@cs.colostate.edu}
\url{www.cs.colostate.edu/~dane}
\name{MoGdisc}
\alias{MoG discriminant functions}
\alias{makeMoG.unsupervised.desc}
\alias{makeMoG.unsupervised.allDesc}
\alias{makeMoG.supervised.desc}
\alias{makeMoG.supervised.allDesc}
\title{MoG discriminant functions}
\description{
  Creates discriminant functions that can be later used to classify new
  data points.  The returned discriminant functions handle standardizing the
  input through computing the class with the highest probability of
  generating each data point.  The discriminant functions need only the
  data points to classify (passed in as a single vector or matrix with
  the data stored as columns).

  The supervised versions of these functions are designed to be used
  when one MoG model is fit to each class.  The unsupervised versions
  should be used when the a single model is used to classify all of the
  classes.

  The functions with the word "all" in them return a list of
  discriminant functions (one for each class) while the others create
  the discriminant function (which simply returns probability of cluster
  or class given a data point) for a given class or cluster.
}
\value{
  \item{$MoGmodel$Pc}{Vector of probabilites.}
  \item{$MoGmodel$$mu}{Matrix of cluster means stored by column.}
  \item{$MoGmodel$Sigma}{List of covariance matrices: one for each
    cluster.}  
  \item{$standardizeF}{Function used to standardize the inputs.}
  }
\arguments{
  \item{X}{Data set matrix with elements by column.}
  \item{C}{Number of clusters.}
  \item{initF}{The function to use to initialize the model}
  \item{covRegF}{The function to use to perform covariance
    regularization.  Function should take entire model as input and
    return entire model.}
}
% \usage{initMoGrandomDatum(X,C)}
\seealso{initMoG,shrinkage,standardization}